Image Class Segmentation via Conditional Random Field over Weighted Histogram Classifier

Image class segmentation is a problem that combines image segmentation and image classification. Conditional random field can be used in image class segmentation to achieve state-of-the-art result, adding high-level information in the course of using low-level cues to conduct segmentation. In this paper we introduce a method using weighted neighborhood histogram on the over-segmented original images. First the image is over-segmented into segments to be performed as basic units. A classifier is then introduced to initialize the confidence value of each class on each pixel with histogram of features. Finally a conditional random field uses it alongside with boundary conditions generate the final result for class segmentation. The method is then tested on PASCAL VOC 07 set and is shown to have state-of-the-art result.